Multistate models for comparing trends in hospitalizations among young adult survivors of colorectal cancer and matched controls
1 Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada
2 Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
3 Department of Surgery, University of Toronto, Toronto, Canada
4 Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
5 Toronto General Hospital, University Health Network, Toronto, Canada
6 Department of Medicine, University of Toronto, Toronto, Canada
7 Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Micheal’s Hospital, Toronto, Canada
Citation and License
BMC Health Services Research 2012, 12:353 doi:10.1186/1472-6963-12-353Published: 9 October 2012
Over the past years, the incidence of colorectal cancer has been increasing among young adults. A large percentage of these patients live at least 5 years after diagnosis, but it is unknown whether their rate of hospitalizations after this 5-year mark is comparable to the general population.
This is a population-based cohort consisting of 917 young adult survivors diagnosed with colorectal cancer in Ontario from 1992–1999 and 4585 matched cancer-free controls. A multistate model is presented to reflect and compare trends in the hospitalization process among survivors and their matched controls.
Analyses under a multistate model indicate that the risk of a subsequent hospital admission increases as the number of prior hospitalizations increases. Among patients who are yet to experience a hospitalization, the rate of admission is 3.47 times higher for YAS than controls (95% CI (2.79, 4.31)). However, among patients that have experienced one and two hospitalizations, the relative rate of a subsequent admission decreases to 3.03 (95% CI (2.01, 4.56)) and 1.90 (95% CI (1.19, 3.03)), respectively.
Young adult survivors of colorectal cancer have an increased risk of experiencing hospitalizations compared to cancer-free controls. However this relative risk decreases as the number of prior hospitalizations increases. The multistate approach is able to use information on the timing of hospitalizations and answer questions that standard Poisson and Negative Binomial models are unable to address.